Is there a reason to choose regular momentum over Nesterov momentum for neural networks?
The book Deep Learning by Goodfellow, Bengio, and Courville says (Sec 8.3.3, p 292 in my copy) states that


  Unfortunately, in the stochastic gradient case, Nesterov momentum does not improve the rate of convergence.


I'm not sure why this is, but the theoretical advantage depends on a convex problem, and from this, it sounds like the practical advantage does too - or at least, that it isn't applicable to typical neural network landscapes.

Perhaps it can be implemented more efficiently, but it seems to me that you need to do parameter updates twice (in order to calculate the gradient in a place you aren't moving two) and thus Nesterov requires more computation and memory than plain ole momentum.